class_docstr: Evaluate metrics for y_true and y_pred.
code: def check_metric(metric: str) -> None:
      if not metric:
          invalidInputError(
              False, f"Got invalid metric name of {metric}!"
          )
      if metric not in Evaluator.metrics_func.keys():
          invalidInputError(
              False, "metric " + metric + " is not supported"
          )
  
imports: from sklearn.metrics import mean_squared_error
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_log_error
  import numpy
  import pandas
  from bigdl.dllib.utils.log4Error import *
  from bigdl.dllib.utils.log4Error import invalidInputError
  from numpy import float64, ndarray
  from typing import List, Tuple, Union, Optional, Callable, Any
label_desc: Use image node_id 2 for calling the Evaluator obj's underlying member method code with example usage: obj.check_metric(metric) without return types